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Record W2943185977 · doi:10.26576/profesi.284

Perencanaan pembelajaran skills lab di STIKES PKU Muhammadiyah Surakarta

2019· article· id· W2943185977 on OpenAlexaff
Ika Kusuma Wardani, Sri Sundari, Moh Afandi

Bibliographic record

VenueProfesi (Profesional Islam) Media Publikasi Penelitian · 2019
Typearticle
Languageid
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPsychologyHumanitiesNursing sciencePedagogyNursingMedicinePhilosophy

Abstract

fetched live from OpenAlex

Pembelajaran skill lab sangat dibutuhkan untuk meningkatkan kemampuan dan kompetensi keperawatan. Penelitian ini bertujuan menggambarkan bagaimana perencanaa, pembelajaran skills lab di di STIKES PKU Muhammadiyah Surakarta. Penelitian ini merupakan penelitian kualititaf dengan menggunakan pendekatan deskriptif. Pengambilan data dilaksanakan dengan beberapa cara yaitu: Focus group discussion melibatkan 12 mahasiswa keperawatan semester 2; wawancara dengan 7 informan; Observasi pembelajaran skill lab dan studi dokumentasi. Selanjutnya data dianalisis dengan metode analisis kualitatif. Hasil penelitian menunjukkan bahwa perencanaan skill lab keperawatan meliputi sumber daya manusia, kurikulum, fasilitas, mahasiswa dan sosialisasi. Kesimpulan penelitian bahwa perencanaan pembelajaran skills lab telah dilakukan dengan sistematis. Perencanaan pembelajaran skills lab harus selalu dilakukan untuk meningkatkan kualitas pembelajaran dan keterampilan mahasiswa keperawatan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0710.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.285
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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